Great post. The companies that are able to get their unique IP, institutional knowledge, and data into a format and architecture that lets them capture all of the gains and progress in AI are going to be in the best position in the future. “the real opportunity is not in picking the best model but instead in building a learning loop on top of models where human capital and token capital compound. You can offload a task, or even a job, but you can never offload your learning. The future of the firm is the ability to compound that learning across people and AI. This requires a new architectural approach where every business is able to build agentic systems that improve over time, while still retaining control over their IP. A company should be able to switch out a “generalist” model without losing the “company veteran” expertise built into their learning system.” We’re all collectively figuring out the right architecture for the future of AI. But it’s clear that so much of the power and value will accrue to wherever can best leverage any AI system against their information. This is also why the applied AI layer will also gain so much value over the coming years.
很棒的文章。那些能够把自己独特的 IP、institutional knowledge（机构知识）和数据整理成一种格式与架构，从而抓住 AI 带来的全部收益与进展的公司，未来会处于最有利的位置。“真正的机会不在于挑选最好的 model，而在于在 model 之上构建一个 learning loop（学习闭环），让 human capital（人力资本）和 token capital（token 资本）实现复利增长。你可以外包一个 task，甚至一份工作，但你永远无法外包自己的学习。企业未来的核心，是能够让这种学习在人与 AI 之间持续复利。这需要一种新的架构方法，让每一家企业都能构建会随时间不断改进的 agentic systems（agent 系统），同时仍然保有对自身 IP 的控制。公司应该能够替换掉一个‘generalist’ model，而不会失去其学习系统中沉淀下来的‘company veteran’级专业经验。” 我们都还在共同摸索适用于 AI 未来的正确架构。但很明显，巨大的能力与价值将会积累到那些最善于让任何 AI system 结合自身信息发挥作用的地方。这也是为什么在未来几年里，applied AI 这一层也会获得如此巨大的价值。
The big winner in all of this is going to be open weights models. This is a huge win for the field, as a risk that was entirely theoretical and untested 2 days ago (that a model could be pulled back), now has a new precedent that’s been set. The game theory the US should highly consider, and the risk with regulating AI at the model layer vs. applied layer, is that other countries now have even more incentive to develop sovereign AI. If at any moment a model can be become unavailable to your country’s users or businesses, this poses very real risk on relying on technology from a particular country. As a result, it forces major countries to charter their own path on AI development, which reduces America’s leadership role in this tech stack over time. The most likely solution that other countries will rely on is open weights models, which currently is generally not coming from the US. America should be considering all of these downstream implications as it decides how and where in the stack to be regulating AI. At the same time, we should be doing a ton more OSS innovation.
这整件事里的最大赢家将会是 open weights models。这对整个领域来说是一次巨大的利好，因为一个在 2 天前还完全停留在理论层面、从未被验证过的风险（即一个 model 可能会被撤回），现在已经有了新的 precedent（先例）。美国应当高度重视其中的 game theory（博弈逻辑），以及在 model layer（模型层）而不是 applied layer（应用层）监管 AI 所带来的风险：其他国家现在会有更强的动力去发展 sovereign AI（主权 AI）。如果一个 model 在任何时刻都可能对你国家的用户或企业变得不可用，那么依赖来自某一个特定国家的技术就会构成非常现实的风险。因此，这会迫使主要国家在 AI 发展上走自己的道路，而这会随着时间推移削弱 America 在这套 tech stack（技术栈）中的领导地位。其他国家最有可能依赖的解决方案是 open weights models，而目前这类模型通常并不是来自美国。America 在决定应当如何以及在这套 stack 的哪些位置监管 AI 时，应该考虑所有这些下游影响。同时，我们也应该大幅增加 OSS 创新。